Nowadays, the electrocardiogram (ECG) is one of the most commonly used tools in clinical practice, since it is a way to measure and diagnose abnormal rhythms of the heart and it is a fundamental part of many clinical instruments such as the cardiotachometer and the arrhythmia monitor.
A typical ECG waveform consists of a P wave indicating atrial depolarization, a QRS complex indicating ventricular depolarization, a T wave indicating ventricular repolarization, and a possible U wave in some cases indicating the extension of the repolarization. The dominant activity of an ECG usually relates to the QRS complex in real time, using medical instruments such as an arrhythmia monitoring system, an ECG stress test system, cardiographs. Accurate detection of the QRS complex of an ECG is significant for all these clinical applications, for example, in an arrhythmia monitoring system, significant false negative and false positive rates can result from faulty QRS detection.
Over the past few years, many techniques have been developed to detect the QRS complex, including power spectrum analysis, bandpass filtering, differentiation, template matching, and waveform feature-dependent real-time techniques. The diversity and complexity of the samples determine the difficulty of accurate QRS detection. However, the conventional real-time detection algorithms are still not very satisfactory in terms of accuracy of detection.
Conventionally, in practical applications, the apparatus used for QRS detection usually selects the electrode lead which contains apparent complex features, for example, one of the standard limb leads, lead II of the standard twelve electrodes recommended by the American Heart Association (AHA). And in some cases, global detection techniques may be used, for example, perpendicular leads II, aVF and V5 are used together to guarantee detection reliability.
However, in a stress testing system, the rapidly increasing muscle activity and mechanical forces acting on the electrode leads usually lead to excessive muscle noise which brings about an unrecognizable waveform. These adverse random artifacts degrade the accuracy of QRS detection.
The deficiencies of the conventional techniques for QRS detection may be clearly understood from FIG. 1, which shows a patient case in a stress testing system conducted at Hoag Hospital, Newport Beach, Calif. The high frequency, low frequency and AC noise of the ECG data has been filtered out by some pre-processing filtering technique. From the top to the bottom, the four waveforms in FIG. 1 are beat detection by using global detection techniques, based on the selected leads II, aVF and V5; detection using lead II only; detection using lead aVF only; and detection using lead V5 only, respectively.
It can be clearly seen that even using global detection techniques, QRS detection is not accurate enough and some important waveform features may be missed, for example, the PVC-like widened QRS complex in leads II, aVF.